list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context: it scopes results to the caller's subscriptions, lists the exact return fields, and implies read-only behavior. It does not contradict annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exactly two sentences, front-loaded with the core purpose, followed by return field details and usage guidance. Every sentence is informative and there is zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only list tool with no output schema, the description provides the return fields explicitly, covers scope, and gives usage context. It does not mention pagination or sorting, but given the tool's simplicity and rich annotations, it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one optional parameter (include_inactive) with 100% schema description coverage. The description does not mention the parameter, but the schema already explains what it does, so no additional value is needed. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb and resource: 'List the caller's active subscriptions.' It clearly distinguishes itself from sibling tools subscribe and unsubscribe by focusing on listing, and it enumerates the exact fields returned. This leaves no ambiguity about the tool's core function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This directs when to use list_subscriptions relative to subscribe and unsubscribe, providing actionable guidance and implied alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purposes, such as the three variants of ask_pipeworx and the multiple prediction market analyzers. While descriptions help differentiate, agents may still struggle to select the correct tool in some cases.
Most tools follow a consistent snake_case verb_noun pattern (e.g., resolve_entity, validate_claim). Minor deviations exist (e.g., forget, remember, recall) but do not significantly harm predictability.
With 35 tools, the server feels heavy for a single named service. The broad scope justifies many tools, but it borders on overwhelming and could benefit from consolidation or clearer grouping.
Despite the name 'disease', the server covers a wide array of domains beyond health. However, while rich in data queries and prediction markets, it lacks obvious tools for domain-specific tasks like disease outbreak tracking or general full-text search.